Package eu.qualimaster.easy.extension.internal

Class Weighting

java.lang.Object

eu.qualimaster.easy.extension.internal.Weighting

All Implemented Interfaces:

net.ssehub.easy.instantiation.core.model.vilTypes.IVilType

public class Weighting
extends Object
implements net.ssehub.easy.instantiation.core.model.vilTypes.IVilType

Weighting functions.

Author:

Holger Eichelberger

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
private static interface	Weighting.IPredictionProcessor	A prediction processor.
private static class	Weighting.MaxProcessor	Implements a maximum prediction processor, i.e., collects the maximum values of the predictions.
private static class	Weighting.UpdateProcessor	Implements an update processor calculating the weighted predictions.

Constructor Summary

Constructors

Constructor	Description
Weighting()

Method Summary

Modifier and Type	Method	Description
private static void	processPredictions(net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double>> predictions, net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double> weighting, Weighting.IPredictionProcessor processor)	Processes predictions.
static net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,Double>	weightAll(net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double>> predictions, net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double> weighting)	Implements a weighting for mass predictions.
static net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,Double>	weightAll(net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double>> predictions, net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double> weighting, net.ssehub.easy.instantiation.core.model.vilTypes.Set<eu.qualimaster.observables.IObservable> costs)	Implements a weighting for mass predictions.
static Map<String,Double>	weightAllImpl(net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double>> predictions, net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double> weighting, net.ssehub.easy.instantiation.core.model.vilTypes.Set<eu.qualimaster.observables.IObservable> costs)	Implements a weighting of mass predictions.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

Weighting

public Weighting()

Method Details

weightAll

public static net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,Double> weightAll(net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double>> predictions,
 net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double> weighting)

Implements a weighting for mass predictions.

Parameters:

predictions - the predictions given as name-observable-prediction mapping, may be null, entries may be null

weighting - the weighting of the observables, negative weights invert the value by subtracting from the respective maximum

Returns:

the "best" solution in terms of the name as maximum of weighted average sums

weightAll

public static net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,Double> weightAll(net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double>> predictions,
 net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double> weighting,
 net.ssehub.easy.instantiation.core.model.vilTypes.Set<eu.qualimaster.observables.IObservable> costs)

Implements a weighting for mass predictions.

Parameters:

predictions - the predictions given as name-observable-prediction mapping, may be null, entries may be null

weighting - the weighting of the observables, negative weights invert the value by subtracting from the respective maximum

costs - the costs, i.e., observables to be counted negative

Returns:

the "best" solution in terms of the name as maximum of weighted average sums

weightAllImpl

public static Map<String,Double> weightAllImpl(net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double>> predictions,
 net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double> weighting,
 net.ssehub.easy.instantiation.core.model.vilTypes.Set<eu.qualimaster.observables.IObservable> costs)

Implements a weighting of mass predictions.

Parameters:

predictions - the predictions given as name-observable-prediction mapping, may be null, entries may be null

weighting - the weighting of the observables, negative weights invert the value by subtracting from the respective maximum

costs - the costs, i.e., observables to be counted negative (may be null for no costs)

Returns:

the "best" solution in terms of the name as the maximum the average weighted value if no costs, the maximum weighted sum if costs

processPredictions

private static void processPredictions(net.ssehub.easy.instantiation.core.model.vilTypes.Map<String,net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double>> predictions,
 net.ssehub.easy.instantiation.core.model.vilTypes.Map<eu.qualimaster.observables.IObservable,Double> weighting,
 Weighting.IPredictionProcessor processor)

Processes predictions.

Parameters:

predictions - the predictions

weighting - the weighting, negative weights invert the value by subtracting from the respective maximum

processor - the prediction processor